The world around us is filled with participating media that attenuates and scatters light as it travels from light sources, to surfaces, and finally to our eyes. Simulating this radiative transport in heterogeneous participating media, such as smoke, clouds, nuclear reactor housings, biological tissue, or other volumetric datasets, is important to many fields. For example, simulating radiative transport in heterogeneous participating media may be important in medical physics, neutron transport, scientific visualization, and film and visual effects production.
Monte Carlo path sampling approaches are practical approaches for accurately approximating this light transport in a scene. Central to Monte Carlo path sampling approaches, however, is a need to evaluate transmittance (fractional visibility) between two points in the scene. Unfortunately, in heterogeneous media, a tremendous numerical approximation is necessary, and relatively little research has been done on performing this critical operation efficiently.
As for other approaches, traditional ray marching techniques result in unpredictable, systematic bias and require many fine steps in high-resolution data. Also, unbiased free-flight sampling techniques like delta tracking can be adapted to estimate transmittance, but unfortunately they result in coarse binary estimators with high variance. As such, both of these options lead to either substantially increased computation times or artifacts in the form of bias or noise.